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Numerical weather predictions (NWP) are systematically subject to errors dueto the deterministic solutions used by numerical models to simulate theatmosphere. Statistical postprocessing techniques are widely used nowadays forNWP calibration. However, time-varying bias is usually not accommodated by suchmodels. Its calibration performance is also sensitive to the temporal windowused for training. This paper proposes space-time models that extend the mainstatistical postprocessing approaches to calibrate NWP model outputs.Trans-Gaussian random fields are considered to account for meteorologicalvariables with asymmetric behavior. Data augmentation is used to account forcensuring in the response variable. The benefits of the proposed extensions areillustrated through the calibration of hourly 10 m wind speed forecasts inSoutheastern Brazil coming from the Eta model.

Space-time calibration of wind speed forecasts from regional climate models